class_docstr: Class for the visualization of Pareto models. Handles the intermediary steps
  between having the results and creating the plot.
  
  Parameters
  ----------
  results : dict[str, dict[str, Result]]
      A dictionary of results, obtained from
      aequitas.fairflow.utils.artifacts.read_results.
  dataset : str
      Name of the dataset to be used in the Pareto plot.
  method : str, optional
      Name of the method to plot. If none, all methods will be plotted.
  fairness_metric : {"Predictive Equality", "Equal Opportunity", "Demographic Parity"}
      The default fairness metric to use in the Pareto plot.
  performance_metric : {"TPR", "FPR", "FNR", "Accuracy", "Precision"}
      The default performance metric to use in the Pareto plot.
  alpha : float, optional
      The alpha value to use in the Pareto plot.
  direction : {"minimize", "maximize"}, optional
      The direction to use in the Pareto plot.
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docstr: Render interactive application to explore results of hyperparameter
  optimization search.
docstr_tok: ["Render","interactive","application","to","explore","results","of","hyperparameter","optimization","search","."]
code: def visualize(self, **kwargs):
      """Render interactive application to explore results of hyperparameter
      optimization search.
      """
  
      from .visualize import visualize
  
      return visualize(self, **kwargs)
  
file_path: aequitas/src/aequitas/fairflow/plots/pareto/plot.py
imports: from typing import Any, Literal, Optional, Union
  from pathlib import Path
  import numpy
  import pandas
  from ...evaluation import Result
  from ....bias import Bias
  from ....group import Group
  from ....plot import summary
label_desc: Use image node_id 7 for calling the Plot obj's underlying member method code with example usage: obj.visualize() and returns: visualize